Previous studies [1]}, [2]} compared the heatmaps of each explainability method for a given sample and concluded that Deep Taylor performed the best for retinal oct. There are also studies showing the effect of uncertainty for diagnosis from retinal fundus images [3]}. The present study is one of the first to report both the explanations and uncertainty analysis. It is observed that the samples classified with higher certainty also have more relevant heatmaps. Some examples covering all 5 classes and some typical cases are shown in Figure . A greater emphasis is laid on the ability to identify model errors using uncertainty and explanations. Figure  shows the retinal oct of a normal eye for reference.
